This AI doesn’t just translate languages, it invents brand-new ones


Ever wondered what a language built entirely by AI would sound like? A team of researchers just made a tool that answers exactly that question. A new paper published in the Proceedings of the Association for Computational Linguistics introduces ConlangCrafter, a tool that uses large language models to build brand new languages complete with their own sounds, grammar, and vocabulary.

Morris Alper, the paper’s lead author and soon-to-be assistant professor at the University of Miami, explained that the goal was to create languages with features you don’t normally find in the ones we already speak. 

Alongside co-authors Moran Yanuka, Raja Giryes, and Gašper Beguš, Alper has already used ConlangCrafter to whip up more than 60 languages, and the code is public if you want to try making one yourself.

How does the AI actually build a language?

You can hand ConlangCrafter specific instructions, and it runs with them. The team tried a language with zero consonant sounds, and even one designed for an alien squid-like species that communicates through color and gesture instead of speech.

Once a language takes shape, ConlangCrafter translates sentences into it, checks its own work, and fixes inconsistencies along the way, all while updating a running rulebook of the language’s grammar.

Alper says the trick was to break the massive task into smaller pieces rather than just asking an AI to generate a language and hoping for the best. “We split the problem apart and have the LLMs solve each sub-problem and combine them together,” he said.

Why would anyone need a made-up language?

Beyond being a fun party trick, this could be a real win for writers and filmmakers who need convincing fictional languages, think Game of Thrones or Lord of the Rings. Researchers also see it helping study poorly documented languages and how languages evolve over time.



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Recent Reviews


YouTube has an AI slop problem, and its crackdown is catching legitimate creators in the crossfire. Faceless channels, where no human host ever appears on screen, have existed for years and are not inherently AI-generated.

Many are run by solo creators who simply prefer to stay anonymous. The problem is that AI tools made it easy to flood the platform with low-effort faceless content at scale, and YouTube’s algorithm is now penalizing the format as a whole.

How bad is the AI slop problem on YouTube?

A Kapwing study found that roughly 21% of the first 500 videos recommended to a new YouTube account were classified as AI slop, while 33% fell into a broader brainrot category. The problem extends to children, too, as more than 40% of YouTube Shorts recommended to kids in a 15-minute session contained low-quality AI content.

YouTube’s response has been to tweak its algorithm to favor videos with real human faces on camera, which is hitting faceless creators even when their content is entirely human-made.

How is YouTube tackling its AI slop problem?

YouTube is now testing a new pop-up on mobile that asks viewers to rate whether a video feels like AI slop, on a scale from “not at all” to “extremely.” The idea sounds reasonable, but crowdsourcing AI detection has real problems. People are bad at spotting AI content, and they are getting worse at it as AI capabilities continue to improve.

There are also legitimate concerns that YouTube could use this viewer feedback as training data for its own AI models, potentially making future AI-generated content even harder to spot.

🚨 Did you just see what YouTube did?

YouTube isn’t banning AI slop.. They’re making you label it so they can train their next model to not look like slop.

Read that again…

You flag the bad AI content. YouTube collects it. Google feeds it into Veo 4… Then next year their… https://t.co/8UC2J3mjjv pic.twitter.com/mIrTChqC1b

— Tuki (@TukiFromKL) March 17, 2026

Meanwhile, faceless creators are scrambling to adapt. According to The Hollywood Reporter, some are hiring cheap on-camera hosts through platforms like Fiverr and Upwork. Others are doubling down on niche educational content, which has held up better than broad content farms.

The AI text-to-video space is still valued at enormous sums, with Higgsfield AI alone sitting at $1 billion, but on YouTube, the math for faceless creators is getting harder to work out every month.



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